Articular cartilage is an important load-bearing tissue distributed on the surface of diarthrodial joints. Due to its avascular, aneural and non-lymphatic features, cartilage has limited self-regenerative properties. To date, the utilization of biomaterials to aid in cartilage regeneration, especially through the use of injectable scaffolds, has attracted considerable attention. Various materials, therapeutics and fabrication approaches have emerged with a focus on manipulating the cartilage microenvironment to induce the formation of cartilaginous structures that have similar properties to the native tissues. In particular, the design and fabrication of injectable hydrogel-based scaffolds have advanced in recent years with the aim of enhancing its therapeutic efficacy and improving its ease of administration. This review summarizes recent progress in these efforts, including the structural improvement of scaffolds, network cross-linking techniques and strategies for controlled release, which present new opportunities for the development of injectable scaffolds for cartilage regeneration. Articular cartilage is a highly organized tissue which has remarkable load-bearing and low friction properties that allow for smooth movement of diarthrodial joints , 2. The Currently, strategies of repairing cartilage defects include debridement and lavage, microfracture, as well as autografts (cell and tissue transplantation) . AlthougTo further expand the utilization of biomaterials in cartilage regeneration, injectable hydrogel-based scaffolds have attracted considerable attention these years in cartilage tissue engineering , 14. HydAn ideal injectable scaffold for cartilage regeneration should typically meet the following criteria: (i) ease of administration under physiological conditions, (ii) guaranteed injectability , (iii) excellent biocompatibility and potential biodegradability, (iv) the ability to mimic cartilaginous ECM features and promote chondrogenic potential of cells, (v) the ability to easily fill defect sites inside the joint and integrate with the surrounding native cartilage tissue rather than shifting readily and (vi) a sustained release profile if associated with local drug delivery , 27–29. Hydrogels possess high water content and elastic properties with cross-linked, multiporous networks . The potet al.  prepared two kinds of HA-based injectable hydrogels by Michael addition, using the amino derivative of HA (HA-EDA), α-elastin-grafted HA-EDA and α,β-poly(N-2-hydroxyethyl)-dl-aspartamide derivatized with divinylsulfone. The controllable swelling and degradation kinetics and its ability to integrate articular chondrocytes of the hydrogel suggested that this scaffold processed desirable properties for cartilage regeneration.The Michael addition reaction has been commonly used to prepare injectable hydrogels, ascribed to its mild reaction condition and controllable reaction time . Jin et et al.  reportedet al. showed that the HRP-mediated cross-linking systems can covalently bind the phenol-conjugated polymers (heparin-tyramine and dextran-tyramine conjugates) to the ECM proteins of the surrounding tissues, which is beneficial in maintaining the structural integrity for arthroscopic cartilage repair [The enzyme-catalyzed chemical cross-linking method has attracted increasing attention for hydrogelation, due to its fast gelation rate, high site specificity, ability to work at normal physiological conditions and low cytotoxicity . There he repair .et al. [et al. [In vivo implantation of chondrocyte-loaded SerMA hydrogels adequately formed artificial cartilages. Although UV-mediated cross-linking is characterized by low cytotoxicity, UV radiation may still have a negative influence on cells, proteins and tissues. Hence, considerable attempts in visible-light-initiated polymerization for cartilage repair have been investigated. For instance, Park et al. [et al. [Photo-cross-linking involves multiple steps, including initiation, propagation and termination, under light illumination. This method requires the introduction of free radical groups, such as vinyl and methacrylate residues together with photo-initiators. In recent years, the photo-cross-linking method has been widely applied to synthesize injectable hydrogels for cartilage tissue engineering owing to the flexible ability to control the timing and location of hydrogel cross-linking . For exaet al.  reported [et al.  designedk et al.  reported [et al.  also repHydrogels are versatile and their various properties, such as high water content, biodegradability, porosity and biocompatibility, allow them to be used often for cell therapy , 120. Inet al. [in vitro. During in vitro culture, chondrogenesis occurred with the formation of cartilage ECM, including type II collagen and aggrecan, which were homogenously distributed throughout the entire hydrogel. Roberts et al. [b-PEG-b-oligo(lactic acid) improved the formation of a cartilage matrix consisting of aggrecan and collagen types II/VI. Although chondrocyte-based biomaterial therapy has demonstrated promising in cartilage tissue engineering, two notable limitations should be considered. First, chondrocyte harvesting involves collecting healthy cartilage tissues from non-load-bearing areas and long-term in vitro culturing (∼1 month) [Autologous chondrocyte implantation has been successfully used in clinic to treat cartilage defects . Howeveret al.  developes et al.  demonstr1 month) , 140. Beet al. [β1 and bone morphogenetic protein) created a synergistic environment for chondrogenesis. The encapsulated ESCs were able to differentiate into chondrogenic cells and promote the production of neocartilage ECM [in vitro research has demonstrated that MSC proliferation and differentiation potential decreases with aging and with aging-related diseases [Biomedical therapies incorporating stem cells and hydrogels for cartilage regeneration commonly include ESCs, MSCs, induced pluripotent stem cells (iPSCs) and predifferentiated MSCs. ESCs, isolated from the tissues of early embryos, show an unlimited self-renewal capacity while maintaining a pluripotent differentiation potential . Howeveret al.  have replage ECM . MSCs, dlage ECM , 143. MSlage ECM . Ample slage ECM , 144–147diseases , likely et al. [Recently, iPSCs have attracted significant attention because they exhibit pluripotency that is quite similar to ESCs in terms of multiple differentiation routes, thus resulting in increasingly widespread applications in regenerative medicine, which can be obtained from somatic cells including fibroblasts , 149. Xuet al.  have demet al. . Recentlet al. . HoweverMany therapeutics exhibit limited efficacy due to the rapid clearance of the drugs in joints. Injectable scaffolds, on the other hand, can sustain drug release and extend the drug retention time. Numerous studies have investigated natural and synthetic biomaterials to develop scaffolds with unique properties, such as improved joint articular dwelling time with sustained drug release while ameliorating the biodegradation of delivery systems. Strategies investigated for the release of biologics with biological activity for treating cartilage defects have developed from simple bolus injections into the focal cartilage defect to multifunctional delivery systems.et al. [Microparticles (MPs) and NPs are desirable formulations for controlled drug release due to their high surface area to volume ratios, small dimensions, high drug encapsulating efficiencies and the capacity to quickly respond to surrounding environmental stimuli, such as temperature, pH, magnetic fields or ultrasound . Recentlet al.  have recet al. . Recentlet al. . This inet al. .in vivo. Second, to enhance the efficiency and duration of the delivery of growth factors or other pharmaceuticals, advanced formulations such as MPs and NPs have been investigated in scaffolds for controlled drug delivery. These advances have also garnered interest in presenting biochemical cues in a controllable manner.To date, injectable scaffolds have provided a promising therapeutic platform for cartilage regeneration. As surveyed above, a number of hydrogel-based scaffolds have been developed with inherent capabilities in cartilaginous tissue engineering, and sufficient mechanical properties for repairing cartilage defects to restore normal joint function. First, to enhance the mechanical properties of scaffolds, traditional single-network hydrogels have been supplemented with either additional networks or mixtures of polymers, and many nanocomposites have been utilized to vary the mechanical properties of scaffolds. These strategies have also been used to produce hydrogels which can improve the integration with surrounding cartilage while promoting chondrogenesis of stem cells encapsulated in hydrogels de novo ECM could compromise their mechanical stability and long-term therapeutic efficacy. One option to overcome this issue is to incorporate appropriate exogenous cells, such as MSCs, within these scaffolds, which could potentially replace the scaffolds as they degrade with newly formed tissue. Third, signaling pathways and particular mechanisms from stem cells to specific cartilaginous cells need further in-depth understanding. It emphasizes more fundamental biological studies of cartilage development and regeneration, which could significantly contribute to the optimization of the injectable scaffolds in the long run. It is also essential to highlight the potential translation of the systems at the beginning of the design. Factors, such as biocompatibility of materials, ease of administration, feasibility of large-scale manufacturing and overall cost should be thoroughly evaluated. Lastly, the next generation of cartilage tissue engineering could be combined with noninvasive/minimally invasive diagnostic technologies to provide real-time assessment of the disease status and overall treatment performance, leading to personalized therapy.Looking ahead, there are still limitations of injectable scaffolds that restrict the complete regeneration of articular cartilage. First, it is essential that the injectable scaffolds can fill the defect area with a smooth interface that is similar to the native cartilage, without integrating into the surrounding healthy tissue. Second, the progressive degradation of hydrogels before they can be replaced by the


Oceanic mesoscale eddies are common, especially in areas where zonal currents with meridional shear exists. The nonlinear effects complicate the analysis of mesoscale eddy dynamics. This study proposes a solitary (eddy) solution based on an asymptotic expansion of the nonlinear potential vorticity equation with a constant meridional shear of zonal current. This solution reveals several important consequences. For example, cyclonic (anticyclonic) eddies can be generated by the negative (positive) shear of the zonal current. Furthermore, the meridional structure of an eddy is asymmetrical, and the center of a cyclonic (anticyclonic) eddy tilts poleward (equatorward). Eddy width is inversely proportional to shear intensity. Eddy phase speed is proportional to shear intensity and the wave amplitude, and their spatial distribution show band-like pattern as they propagate westward. This nonlinear solitary solution is an extension of classical linear Rossby theory. Moreover, these findings could be applied to other areas with similar zonal current shear. This finding has triggered a number of theoretical works exploring the enhancement with consideration of background flow2, bottom topography3, other factors7. Killworth and Blundell9 provided a comprehensive theory (including both background flow and topography effects) that showed a large-scale bottom slope is insufficient to explain the observed rate of propagation. Aoki et al.10 analyzed the output of a high-resolution ocean general circulation model, and argued that the dominant factor enhancing the phase speed is bottom pressure decoupling related to rough bottom topography, while north of 30°N, the background flow makes a strong contribution to the enhancement of phase speed. However, some of the observed Rossby wave features cannot be interpreted using classical Rossby wave theory. Subsequent observations of high-resolution SSH fields, constructed by merging the measurements of two simultaneously operating altimeters, have revealed that westward-propagating signals can be identified as nonlinear eddies12. Previous theoretical research has focused mainly on linear models or numerical simulations13 because of the difficulty in solving nonlinear problems.Mesoscale eddies are ubiquitous in the ocean, with typical horizontal scales on the order of 100 kilometers and timescales on the order of a month. Mesoscale eddies play a key role in the transport and mixing of momentum and tracers across the World Ocean. Satellite observations of sea surface height (SSH) have enabled visualization of the westward phase propagation of SSH anomalies. It has been revealed that the phase speeds of isolated abnormal SSHs that are faster than expected based on classic mid-latitude Rossby wave theory14 found that solitary waves are possible in a fluid system confined by rigid boundaries, and in certain atmospheric motions. Boyd15 applied the multiple scales method to the primitive equations to show that long, small amplitude Rossby waves are governed by either the Korteweg-de Vries (KdV) equation or modified KdV (mKdV) equation. Helfrich et al.17 developed an asymptotic time-dependent theory for coherent structures on a marginally stable baroclinic flow. Yang at el.18 discussed the interaction of algebraic solitary Rossby waves with topography and atmospheric blocking. Hodyss and Nathan19 studied the dynamics of solitary Rossby waves in a meridionally sheared, zonally varying jet flows, and showed that the zonally varying background flow yielded three general classes of behavior: reflection, transmission, or trapping. They show the oscillatory decay, creation and steady state of solitary Rossby waves, when there is zonally varying jet flow. The KdV equation also has been obtained by Benney20, Redekopp21, Yamagata22, and Warn23. Based on KdV equation, the authors investigated the spatial structure and propagation characteristics of solitary waves, and point out that the background flow shear is a necessary condition for the existence of solitary waves.The nonlinear term reflects important physical phenomena. Long24, a nonlinear model and its corresponding asymptotic solution are derived in this paper. By using the asymptotic solution, the influence of background flow shear is discussed in detail. Furthermore, we examine some typical solutions and compare them with classical Rossby wave theory in the interpretation of the observed westward-propagating features.Despite recent progress, how the varying background flows influence the phase speed, wave width and the symmetry of solitary Rossby wave, however, remains to be addressed. Based on the reduced-gravity potential vorticity equation, and using scale transformations and the perturbation expansions method25, where cyclonic eddies might exist in one latitude band and anticyclonic eddies exist in the adjacent latitude band  zone is one of the regions with most eddy activity. Qiu et al.30 pointed out that in the STCC area, both submesoscale and mesoscale eddies are influenced by baroclinic instability, and show seasonal variation.Figure 31, it does not show Rossby wave dispersion32, and it demonstrates a westward increase in amplitude and phase speed1.Moreover it has been reported that the westward-propagating wave  has some meridional asymmetryHowever, the influences of background flow shear on the spatial structure, propagation velocity and wave width of solitary Rossby waves remain absent. The objectives of this article are to elucidate the background flow horizontal shear effects on Rossby waves in a nonlinear regime, and to interpret those observed characteristics of isolated anomalies that classical theory cannot explain.33:L is a characteristic length scale of the motion, Rd is the Rossby deformation radius, ψ is the stream function, β = β*L2/U is the gradient of the Coriolis parameter, and U is the characteristic horizontal velocity scale of the motion.We consider the following nondimensional reduced-gravity quasi-geostrophic potential vorticity equationThe horizontal velocity can be obtained fromy* = yL, where L represents domain size. The boundary condition isIn this paper, the dimensional variables are denoted by an asterisk, for example The background stream function has the formψ as a disturbance stream εϕ pre-imposed on the zonal flow u:ε represents the Rossby number, which is a small parameter. Substitution of (4) into (1) yieldsWe take the total stream function 15:c0 is the linear Rossby wave phase speed, which can be determined by solving following eigenvalue problem. Using the multiple scale method, we write the disturbance stream function asϕ1 = Aψ1(y), A is the amplitude, and ψ1(y) is the meridional structure of the waves. After a series of manipulations (see Supplementary Equations 1), we obtain the KdV equation21 for the amplitude Aψ1(y)In order to balance the nonlinearities and dispersions, we introduce the following slow space and time scales20, Redekopp27, Yamagata22, and Warn23 also obtained the same KdV equation as equation . The governing equation of the amplitude (7) can be reduced to:If there is no shear in the background flow, the solution of the eigenvalue equations  can be ouy ≠ 0, and as u is function of variable y, it is difficult to obtain the analytic solution of eigenvalue equations  is asymmetry because of the basic flow shear.O(δ2) term gives:Substituting  and 12)12) into m represents the modulus of Jacobi elliptic function, A0 is the amplitude of the waves, m→1, cnoidal waves solution  must be positive to keep this width physical meaningful. In other words, if the background flow shear is positive (δ > 0), A0 must be positive too. Positive A0 means there are anticyclonic solitary Rossby waves . On the other hand, if it is negative (δ < 0), there are cyclonic solitary Rossby waves . When n→∞, the width w will tend to be zero and the sea surface will seem like flat that corresponds to the background geostrophic current. When n = 1, the solitary wave shows the largest width. When ε→0(the current is absolutely in geostrophy), the w→∞ and it means that given a constant energy of solitary wave, the amplitude will be negligible and the solution is the background geostrophic current.Using Jacobi elliptic function expansion methods and equation , the cnoThe phase speed of the solitary Rossby waves isc0. This feature agrees with the observations that large-amplitude solitary Rossby waves propagate faster than small-amplitude waves1. A notable feature of solitary Rossby waves that distinguishes them from linear Rossby waves is that their speed is dependent on amplitude. Furthermore, this solution shows another kind of solitary Rossby wave whose phase speed is slower than the classic Rossby phase speed. This solution remains to be verified by observations.It is important to note that the phase speed of solitary Rossby wave proportional to its amplitude and could be faster or slower than the classical Rossby wave phase speed. When n must be an odd number, otherwise, a solitary Rossby wave cannot exist. The present analysis takes n = 1 and it focuses on latitude 23°N in the North Pacific. At this latitude, the typical values of parameters for oceanic solitary Rossby waves are β* = 2.1 × 10−11m−1s−1, L = 1.6 × 105m, ε = 0.2, U = 0.045 ms−1 and Rd = 5 × 104m34. We take |A0| = 1 and u* as follows:u* is smaller than the linear long Rossby wave phase velocity From the first equation of , n must δ < 0, the meridional structure tilts northward and when δ > 0, the meridional structure tilts southward. When δ = 0, which reduces to linear Rossby waves, the meridional structure has north–south symmetry.The meridional structure of Rossby waves can be determined from . From FiA in (m (δ = 0.1) and δ (m = 0.8), when X0 = 0, t = 0, and |A0| = 1, is shown in Fig. m represents the modulus of the cnoidal waves , cyclonic solitary Rossby waves occurs (A < 0). When the flow shear is positive (δ > 0), anticyclonic solitary Rossby waves occurs (A>0). When δ = 0, the model degenerates into the linear model and either anticyclonic or cyclonic solitary Rossby waves exist (the amplitude can be arbitrary).The amplitude A in  of Rossbal waves , which iA0 is, the smaller the width is. It means that this solution permits strong SSH anomalies with a small diameter. Moreover, it is interesting to note that the width of the solitary wave is proportional to the gradient of the planetary vorticity β and u0. Thus, the wave width tends to increases equatorward. However, this equation is not applicable to equator because geostrophic balance in the lowest order needed by equation , the phase speed is proportional to the amplitude (|A0|), and the greater the shear, the more significant the effect of amplitude on the phase speed. If δ = 0, the model degenerates into the linear model and the amplitude has no effect on phase speed. For a given amplitude, the phase speed is proportional to the intensity of the background flow shear (|δ|), and the minimum value of phase speed is obtained when δ = 0, which is exactly the result of the linear model. It is worth noting the small difference in phase speed when δ takes a different sign. This occurs because the flow shear changes the background flow, which further changes the phase speed because of the Doppler shift. However, the flow shear effect is much smaller than the amplitude effect on the phase speed.Figure 12. In addition, the background flow shear also contributes to the band-like eddy polarity pattern in the STCC zone , because of the weak shear itself. A stronger shear is expected to correspond to more significant modulation of wave properties, but this will need to be confirmed by a separate study. Using sea-surface height fields constructed from the merged TOPEX/Poseidon and ERS-1/2 altimeter datasets, Chelton et al.11 investigated global ocean mesoscale eddies in detail. The propagation speeds and directions of the observed eddies are consistent with the theories for nonlinear dynamics, which predict that eddies should propagate westward with little meridional deflection at the phase speeds of nondispersive baroclinic Rossby waves13. The poleward (equatorward) movement of westward propagating cyclonic (anticyclonic) eddies are expected from the combination of the β effect and self advection. The observed weak dispersion in wavenumber frequency spectra of SSH also confirm the property of nondispersion32, because the eddies retain their shapes as they propagate the energy at every wavenumber propagates at the same speed. The eddies generation mechanism is most likely due to the instability of the background currents. The width of solitary wave is inversely related to the background current shear strength. If the shear is too strong, it will produce sub-mesoscale eddies, such as the distribution of eddies across the Kuroshio in the East China Sea35. The asymmetrically distribution of eddies across the Kuroshio in the East China Sea (predominant cyclonic eddies are on the western sides of Kuroshio and anticyclonic eddies are on the eastern sides of Kuroshio) is also related to the different horizontal velocity shear on both sides of the Kuroshio axis35. These effects of background flow shear on solitary Rossby waves are summarized in Fig. The above discussion shows the background flow shear has significant impact on the Rossby waves. Consideration of the nonlinear effects shows that solitary Rossby waves exist with properties that are quite different from classic Rossby waves but consistent with satellite observations. If the background flow shear is positive (negative), anticyclonic (cyclonic) solitary Rossby waves exist and their meridional structure is tilted southward (northward), which is consistent with the observations that the eddies propagate westward at approximately the phase speed of linear baroclinic Rossby waves, and cyclonic (anticyclonic) eddies deviate slightly poleward (equatorward) with respect to the latitude circleL = 5 × 105 m, n = 3, and δ = 0.1, and the other symbols have the same values as discussed above. The background flow shear effects on amplitude, width, and phase speed are consistent with the conclusions derived when n = 1; the obvious difference is the meridional structure ψ1. The meridional structures and streamlines of solitary Rossby waves are illustrated in Fig. δ = 0.1, the meridional structure tilts southward, and both the amplitude and the width are larger in the north. In contrast, when δ = −0.1, the meridional structure tilts northward, and both the amplitude and the width are larger in the south. It is reasonable that the meridional shear of zonal current determine the southward or northward tilting.In this section, we choose u = u0 +δy, the effects of flow shear on the meridional structure, amplitude, width, and phase speed of solitary Rossby waves were studied.In this paper, by employing perturbation expansions and scale transformations of the time and space method, an asymptotic solution of the potential vorticity equation was derived. For a specific background zonal flow δ|). Solitary Rossby waves are non-dispersive waves, and the width of solitary Rossby waves is inversely proportional to the intensity of the flow shear (|δ|). Chelton et al.1 pointed out that the standard theory for free, linear Rossby waves is an incomplete description of the observed waves. Observed characteristics of isolated anomalies were found to be broadly consistent with our nonlinear quasi-geostrophic theory, which suggest the importance of nonlinear dynamics. Based on the analytical solution, the background flow shear effect can be used to explain the band-like distribution of mesoscae eddies in the subtropical counter current zone.Background flow shear is required for the existence of solitary Rossby waves. Cyclonic (anticyclonic) solitary Rossby waves exist in a cyclonic (anticyclonic) mean flow, and their meridional structure tilts northward (southward). The phase speed of solitary Rossby waves is proportional to the amplitude, and the greater the flow shear, the more significant the effect of amplitude on phase speed. The phase speed is also proportional to the intensity of the flow shear  equation may imply instability due to singular behavior near a potential critical layer, i.e. a latitude where u = c0. However, for eastward currents (u > 0), this singular behavior is absent. Therefore the solitary wave solution is nearly absent in area where the zonal current flows eastward, while solitary waves (eddies) can be expected to be found in the westward flowing zonal current area. The solution is only applicable to the area with a weak zonal shear, and strongly sheared flows may exhibit different properties. Flow with a strong horizontal shear will also be subject to barotropic instability, which can further modify the solution. Furthermore, it is especially favorable in the westward flowing zonal current area. In the STCC zone, solitary eddies mainly concentrate in the areas with weak background flow (north of 20°N), while the number of solitary eddies in the area with strong westward flow (south of 20°N) is less .Supplementary Information


Microcystis aeruginosa. Even in microcystin-producing strains, other classes of oligopeptides including cyanopeptolins, aeruginosins, and aerucyclamides, were often the more dominant compounds. The distinct and large variation between strains of the same widespread species highlights the need to characterize the metabolome of a larger number of cyanobacteria, especially as several metabolites other than microcystins can affect ecological and human health.Cyanobacteria are notorious for their potential to produce hepatotoxic microcystins (MCs), but other bioactive compounds synthesized in the cells could be as toxic, and thus present interest for characterization. Ultra performance liquid chromatography and high-resolution accurate mass spectrometry (UPLC-QTOF-MS/MS) combined with untargeted analysis was used to compare the metabolomes of five different strains of the common bloom-forming cyanobacterium,  Visible accumulations of cyanobacterial biomass in the form of surface blooms have been increasing over the past few decades ,2, mainlMicrocystis, Dolichospermum (Anabaena), and Planktothrix -MC-LR and [Leu1]-MC-LR, three unconfirmed variants of microcystins were detected. Those metabolites were identified as microcystins because of the presence of the ADDA fragment  include more than 82 variants , as variM. aeruginosa, M. viridis and Planktothrix (Oscillatoria) agardhii -LR variants were bought from Enzo Life Sciences . In addition, an MC standard for the variant [Dha7]-LR  was provided by Dr. S. Sauvé, Département de Chimie, Université de Montréal. All 12 standards had a purity over 95%, and in each case 10 ppm in methanol was prepared as identification references. The oligopeptide nodularin has been used in previous studies as an internal standard to estimate the recovery of microcystins during extraction [Commercially available microcystins  and nodularin were included in this study. Nodularin standard (CAS 118399-22-7) and MCs standards for LR (CAS 101043-37-2) and LA (CAS 96180-79-9) variants were bought from Cayman Chemicals . MC-LF (CAS 154037-70-4), MC-LW (CAS 157622-02-1), MC-LY (CAS 123304-10-9), MC-RR (CAS 111755-37-4), MC-WR (CAS 138234-58-9), MC-YR (CAS 101064-48-6) and [Asptraction ,65,66. IAll LCMS analyses were performed on a UPLC-QTOF system . LCMS system was comprised of an ultra-high-performance liquid chromatograph, upper pressure limit 1200 bars (Acquity UPLC), connected to a quadrupole time of flight mass spectrometer (Xevo G2 QTOF). Chromatographic separations were performed on an Acquity UPLC BEH C18 column . LCMS grade mobile phases were (A) acetonitrile and 0.1% formic acid, and (B) water with 0.1% formic acid. Mobile phases were delivered at 0.8 mL/min with a linear gradient of 5% to 95% A in five minutes at column oven temperature of 50 °C. m/z 50 to m/z 1500 Da. All the data were acquired without mass correction using MassLynx V4.1 SCN918.QTOF was operated at unit resolution. Two separate injections (0.2 μL) were performed to obtain positive and negative ions at collision energies of 6 V and 20–50 V to obtain low and high fragmentation spectra of each signal that was above the specified noise level. Other QTOF parameters were: scan time 0.1 s; sample cone voltage 40 V; capillary voltage 1.2 kV for positive ionization, and 2.5 kV for negative ionization; extraction cone 4-35 V; source temperature, 150 °C; desolvation temperature, 500 °C; desolvation gas flow, 1200 L/h; cone gas flow, 20 L/h. MS data were collected in scan mode from TM (V 8.03) to generate unsupervised  and supervised  plots [Data were collected on the QTOF between 0.3–6 min RT and exported to MarkerLynx to generate a matrix consisting of RT, accurate mass pairs, and peak heights. These raw data were then transferred to EZInfoA) plots . For prihttps://metlin.scripps.edu) was used. For tentative identification of metabolites mass accuracy threshold was set at ± 5 ppm for the parent ion and ± 10 ppm for the respective product ions and finalized by mass fragment and ChemDraw  or as seen in available standards of similar metabolites. To search for unidentified microcystins in the samples, the total ion chromatograms (TIC) were scanned for the ADDA moiety molecular weight (structure available in The identification of microcystin variants in cell culture extracts was performed by matching the (1) RTs, (2) elemental compositions, (3) parent ions, and (4) product ions. METLIN (Scripps Center for Metabolomics:


It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new reference sequences are added to training data, statically trained classifiers must be rerun on all data, resulting in a highly inefficient process. The rich literature of “incremental learning” addresses the need to update an existing classifier to accommodate new data without sacrificing much accuracy compared to retraining the classifier with all data.th of the non-incremental time with no accuracy loss.We demonstrate how classification improves over time by incrementally training a classifier on progressive RefSeq snapshots and testing it on: (a) all known current genomes (as a ground truth set) and (b) a real experimental metagenomic gut sample. We demonstrate that as a classifier model’s knowledge of genomes grows, classification accuracy increases. The proof-of-concept naïve Bayes implementation, when updated yearly, now runs in 1/4It is evident that classification improves by having the most current knowledge at its disposal. Therefore, it is of utmost importance to make classifiers computationally tractable to keep up with the data deluge. The incremental learning classifier can be efficiently updated without the cost of reprocessing nor the access to the existing database and therefore save storage as well as computation resources. Recent advances in genomics have resulted in exponential increases in the rate at which data is collected. Inspired by Zynda , we visuMoreover, driven by advances in technology and significant reductions in the cost of analysis, microbiome research has unlocked a wealth of data in recent years . In factOne of the main challenges in metagenomics is to answer, “Who is there?”: identifying the microorganisms in the sample. Taxonomic classification – classifying the reads in metagenome sequencing data – uses aligners, read mappers, classifiers and other “base techniques” to solve this problem –11. Taxocontinuous assembly approach” (supported by the institution’s infrastructure and the scientific community supplying the data sources) ), the user can specify the number of threads made available to NBC++. The program will then attempt to run as many parallel instances as threads it has available, and load training/testing data into the available memory for all the threads. The scalability of the previous naïve Bayes classifier is improved. NBC++ can now work within a pre-set memory limit. The program will automatically adjust how many reads it loads into memory, dynamically creating multiple “batches” of various sizes, trying to fit as many as possible in one cycle. This is a sub-optimal but necessary operating mode, as dataset sizes often exceed available RAM. The performance penalty of this process in the case of classification is derived from the repeated iterations through the training database – repeated for each batch. The trade-off is to load and unload the same training classes in/from memory multiple times, because we need to classify multiple batches of reads. We describe the detailed implementation below.One way in which NBC++ facilitates computational scalability is by automatically separating reads into batches, depending on the amount of memory the user decides to use. By using the -m [size] argument, the user can instruct NBC++ to keep all batches under the specified memory cap, thus allowing the user to train and classify large datasets on a variety of systems, getting optimal performance on each run without the need for users to manually adjust batch contents. This is particularly helpful when reads in the database have uneven sizes, which would normally make them difficult to manually add to a batch containing a fixed number of reads.To demonstrate this behavior, consider a scenario where the program is given 510 sequences (of greatly varying lengths) and the memory is capped to 32GBs. Figure An additional argument, -n [nbatch] is provided as a manual override for the number of reads to include in a batch. This argument is not compatible with the memory cap option and should only be used if the user prefers to process a fixed number of reads at a time, rather than trying to use all available memory optimally.We also allow for multi-threaded computation through the -t [threads] argument, which prompts NBC++ to create worker threads that will concurrently handle one class each. This option is independent of the memory cap, which means that users should allow for some additional memory to be used by each thread in loading their training class savefiles: total_memory = read_memory_cap + n_threads × savefile_average_size.With these two options enabled, NBC++ will first create a batch of reads that fit within the provided memory cap. The program will then create the specified number of threads, loading one class savefile for each and processing in parallel all reads within the batch. When we’ve finished processing all the reads in the batch, the class savefile is unloaded and the next class is read from disk. The thread will then begin to process reads from the beginning. The batch is discarded when this operation has ran for all existing classes, in which case the program will begin creating a new batch and repeating the process until the entire dataset is processed.The multi-threaded computation along with memory cap is very useful in computing cluster environment. We use a case study here to illustrate the usage. Suppose we allocated 16 cores and a total of 64 GB memory resources to run NBC++. Then, the value we pass to -t [threads] would be 15 since we need 1 core for master process and the rest of 15 can be used for multi-threading. The memory cap should be considered as the memory available solely for testing reads. Given we have 16 cores and 64 GB memory, we have 4 GB per core. If the maximum size of training class savefile (class object of a species derived from training data) is 2 GB. Then 2 GB per core are available for testing. To be safe, we don’t include the memory for master core for testing reads. Then the total amount of memory for testing can be set to 2×15=30 GB. Therefore, we pass 30 GB to -m [size] parameter. In this way, we manage to balance the loading of both save files (training) and testing data in memory.Additional file 1 The number of taxonomic labels per year. The number of updates in the NCBI bacteria genome database on six taxonomic levels, namely, species, genus, family, order, class and phylum. We have a figure for “Accumulative number of updates per year” per taxonomic level  and a figure for “compared with last year, the number of new updates per year” per taxonomic level .Additional file 2 Histogram of the number of genomes per species. We plot the histogram of the number of genomes per species every year. The figures show that many species have only one genome. We designed our experiment so that we can train a model on some genomes of a species and then evaluate the model with other genomes of the same species. Therefore, when we train on those species, we don’t have a testing genome to simulate testing reads from. And when we do simulate testing reads from those genomes, they don’t exist in the training set. To this end, we evaluate our model with two metrics: accuracy for all reads and accuracy for known reads (reads from known species).Additional file 3 RefSeq bacterial genomes published every year. This table shows the release year, the organism name and taxonomic ID of completed genomes in RefSeq Bacterial assembly summary.Additional file 4 The average accuracy per species from 1999 to 2019. The table shows the average accuracy per species for 5-fold cross-validation.Additional file 5 The standard deviation of the accuracy per species from 1999 to 2019. The table shows the standard deviation of the accuracy per species for 5-fold cross-validation.Additional file 6 Profiling results change over time on species level. The NBC incremental learning classifiers trained on different years are evaluated on a real human fecal sample (SRA ID: SRS105153) on species level. The figure shows that the predicted composition of the sample changed over time and is influenced by the training data. Taxa with lower than 5% relative abundance is moved to either “Others: New” category or “Others: Old” category depending on weather the taxa is recently added or was added in previous section.Additional file 7 Profiling results change over time on order level. The NBC incremental learning classifiers trained on different year are evaluated on a real human fecal sample (SRA ID: SRS105153) on order level. The figure shows that the predicted composition of the sample changed over time and is influenced by the training data. Taxa with lower than 5% relative abundance is moved to either “Others: New” category or “Others: Old” category depending on weather the taxa is recently added or was added in previous section.Additional file 8 Misclassified reads – phylum level. We trace our species classification results back to phylum level and most of the reads are assigned to the correct phylum level taxonomic labels. This table shows the reads that got misclassified in phylum level.Additional file 9 15-mer shared with B. aphidiciola (taxid: 9). 15-mer shared between the case example read in “B. aphidiciola (taxid: 9).Additional file 10 15-mer shared with C. botulinum (taxid:1491). 15-mer shared between the case example read in “C. botulinum (taxid:1491).Additional file 11 Blast result for B. aphidiciola (taxid: 9). Blast result for the case example read in “B. aphidiciola (taxid: 9) exclusing strain Schlechtendalia chinensis.Additional file 12 Blast result for C. botulinum (taxid:1491). Blast result for the case example read in “C. botulinum (taxid:1491).Additional file 13 Blast search strategies for B. aphidiciola (taxid: 9). The Blast search strategies for the case example read in “B. aphidiciola (taxid: 9) exclusing strain Schlechtendalia chinensis. The file ends in ASN and the reader can also open it as a normal text file.Additional file 14 Blast search strategies for C. botulinum (taxid:1491). The Blast search strategies for the case example read in “C. botulinum (taxid:1491). The file ends in ASN and the reader can also open it as a normal text file.


Tetragonula carbonaria and Tetragonula hockingsi species in Australia, from Geniotrigona thoracica and Heterotrigona itama in Malaysia and from Tetragonisca angustula in Brazil. The previously unrecognised abundance of trehalulose in stingless bee honeys is concrete evidence that supports some of the reported health attributes of this product. This is the first identification of trehalulose as a major component within a food commodity. This study allows the exploration of the expanded use of stingless bee honey in foods and identifies a bioactive marker for authentication of this honey in associated food standards.Stingless bee (Meliponini) honey has long been considered a high-value functional food, but the perceived therapeutic value has lacked attribution to specific bioactive components. Examination of honey from five different stingless bee species across Neotropical and Indo-Australian regions has enabled for the first time the identification of the unusual disaccharide trehalulose as a major component representing between 13 and 44 g per 100 g of each of these honeys. Trehalulose is an isomer of sucrose with an unusual α-(1 → 1) glucose-fructose glycosidic linkage and known acariogenic and low glycemic index properties. NMR and UPLC-MS/MS analysis unambiguously confirmed the identity of trehalulose isolated from stingless bee honeys sourced across three continents, from  Like the more well-recognised Apis mellifera honeybees, stingless bees live in permanent colonies made up of a single queen and workers, who collect pollen and nectar to feed larvae within the colony and likewise store honey in the hive for this purpose. Honey produced by stingless bees is known by various names such as Meliponine honey, pot-honey, sugarbag honey , and Kelulut honey . Under these and other names, stingless bee honey has a long history of traditional indigenous use with a range of purported therapeutic properties2, including antidiabetic and antioxidant activity4. Various studies have been conducted of physicochemical and nutritional composition of stingless bee honey5, but to date few bioactive components have been identified2. While these studies all acknowledged that the composition of stingless bee honey is different to that of European bee honey, no rigorous identification of the major components and potential therapeutically active compounds has been reported. In addition to the importance of identifying the potential therapeutic components of stingless bee honey, the rapidly increasing consumer demand for stingless bee honey derived products has highlighted the need to produce food standards to enable establishment of authenticity and provenance of such products6.Stingless bees (Meliponini) occur in most tropical and sub-tropical regions, with over 500 species of these eusocial bees distributed across Neotropical, Afrotropical and Indo-Australian regions1) −) m/z 341.1082  was clearly a disaccharide, but did not match any of our initially available disaccharide standards. Previous analysis of stingless bee honeys have suggested that the disaccharide present was the glucose-glucose disaccharide maltose (2)18. However, the improved resolution and mass spectral data provided by our UPLC-MS/MS method demonstrated that the disaccharide present in all five stingless bee honeys, was in fact not maltose. This enigmatic disaccharide eluted with a slightly shorter UPLC retention time compared to a maltose standard  1a) and (1b). Tentative structural assignments were compared with literature enabling the unambiguous assignment of the unknown honey disaccharide as trehalulose  being present at a higher level than 1-O-α-d-glucosylpyranosyl-β-d-fructofuranose tautomer (1b)19. Trehalulose was previously isolated from excrement of sweet potato whitefly Bemisia tabaci by Bates et al.20 These authors reported both 13C and 1H NMR assignments in D2O of three tautomers present in 20:4:1 ratio. Their NMR data were similar to incomplete details reported previously by Cookson19, who had isolated trehalulose from a mixture of glucose, fructose and isomaltulose produced by immobolised microbial cells, with the isolated disaccharide reported to be a 2:1 mixture of fructopyranose and fructofuranose forms by 13C NMR and other methods.1D and 2D NMR analysis of the isolated disaccharide suggested a 1,1-linkage between glucose and fructose monosaccharides with evidence of major/minor tautomers for the fructose ring (1a) and (1b), which were present in a 3.6:1 ratio by integration of the anomeric proton signals for the glucose rings (Table 1a) were irradiated in separate 1D TOCSY experiments to identify the proton signals in each monosaccharide spin system. This allowed full elucidation of the pyranose tautomer of fructose (β-d-Frup) predominant in trehalulose (1), with assigned 13C and 1H NMR data of (1a) shown to be in close agreement to that previously reported by Bates et al.20 for this compound  were partly obscured by the more dominant fructopyranose tautomer (1a) in the isolated trehalulose. Only the anomeric glucosyl proton doublet  could be clearly distinguished. Based on 2D HSQC, TOCSY and COSY experiments, all other protons and carbon shifts of the minor fructofuranose conformer (1b) were assigned as shown in Table 20. A third more minor tautomer with anomeric glucosyl proton doublet  could be seen in our trehalulose NMR spectra, but due to the low concentration and overlap with the other anomeric signals it is not possible to report full data for this compound. This is presumably the component speculated to be the α-furanose tautomer by Bates et al.20.1) with minor disaccharides tentatively identified as sucrose, maltulose and turanose by comparison of NMR chemical shifts with literature22. Isomaltulose (3)22 was not detected in these NMR spectra of trehalulose isolated from stingless bee honey.Close examination of NMR spectra demonstrated that the disaccharide isolated from each of the five stingless bees comprised > 90% trehalulose  MS/MS fragmentation for trehalulose (1) has not previously been reported. UPLC-MS/MS analysis of trehalulose isolated from each of the stingless bee species showed a single unresolved peak for (1a) and (1b) tautomers  structure. Glycosidic bond cleavage of the molecular ion ([M-H]−) m/z 341 of trehalulose (1) produces a dominant m/z 179, with fragmentation of this ion to m/z 161, 143, 131, 119 and 113  differed markedly from that of maltose. Most notably, the m/z 221 peak corresponding to the glucosyl-glycoaldehyde anion observed in the mass spectra of maltose (2) and isomaltulose (3)26, was not observed in the 1 → 1-linked trehalulose (1) spectrum , but the authentic standard erroneously contained a second minor disaccharide peak which proved by UPLC-MS/MS analysis to be an isomaltulose  (3) contaminant. This second peak had identical retention time and MS/MS fragmentation to authentic isomaltulose (3) 24. Isomaltulose (3) and trehalulose (1) are regioisomeric disaccharides which occur concomitantly in reported biological sources of these disaccharides27, and the presence of isomaltulose (3) as a 5% contaminant in the commercial trehalulose standard is understandable. Industrial trehalulose production has been achieved from sucrose using immobilised enzymes, with differing ratios of (3) to (1) produced depending on the enzyme used28.The authentic standard of trehalulose obtained for confirmatory comparison exhibited a major UPLC peak with the same retention time and MS/MS fragmentation as our isolated disaccharide 3) Figs. , 3, incl Figs. , 1) isolated by preparative HPLC from each of our stingless bee honeys contains no evidence of co-occurring isomaltulose (3) in either UPLC-MS/MS chromatograms . This observation highlights the potential of stingless bee honey as a novel source of pure trehalulose (1).It is worth noting that trehalulose  as a major component representing between 13 and 44 g per 100 g of each stingless bee honeys. The highest proportion of trehalulose was present in the sample of Malaysian Geniotrigona thoracica honey in which it represented 84% (w/w) of the total sugar content. The only other sugars present at significant levels were the monosaccharides glucose and fructose.UPLC-MS/MS quantitative analysis of the sugars present in the same five stingless bee honey samples employed in preparative HPLC isolations confirmed the presence of trehalulose  glycosidic bond  as a distinguishing disaccharide in these stingless bee honeys thus provides a ready marker for authenticity, in the same way that specific marker compounds are used to authenticate high value mānuka honey34. Trehalulose therefore represents an ideal indicator of authenticity to be incorporated in the development of relevant stingless bee honey standards6. Previous attempts to develop a fast FTIR-ATR analysis of sugars in H. itama honey were unfortunately ill-founded in the belief that the major disaccharide present in this stingless bee honey was maltose (2)16. However, it is likely that the chemometric PLS regression analysis presented by these authors will still hold true for quantitation against trehalulose , presenting the ready opportunity for the development of a fast FTIR-ATR analysis method for trehalulose (1) in stingless bee honey. This would facilitate the development of a rapid method for the authentication of stingless bee honey products.This is the first isolation of trehalulose from a food source, namely stingless bee honey. Stingless bee honey represents a highly valued food with accredited biological activity, and this reported activity may in part be attributable to the previously unrecognised abundance of trehalulose in these honeys. The presence of trehalulose Tetragonula hockingsi (syn. Trigona hockingsi)35 and Tetragonula carbonaria (syn. Trigona carbonaria)35 honey samples were collected from hives located in suburban backyards in Brisbane, Queensland Australia. Geniotrigona thoracica (syn. Trigona thoracica)35 honey was purchased from a producer in Kampung Rinching Hilir, Selangor, Malaysia. Heterotrigona itama (syn. Trigona itama)35 honey was purchased from a producer in Batang Kali, Selangor, Malaysia. Tetragonisca angustula (syn. Trigona angustula)35 honey (Brazil) was provided as a gift. All stingless bee honey samples were stored in a refrigerator (4 °C) before analysis.Authentic trehalulose (specified by supplier > 90% purity) was purchased from Biosynth Carbosynth . Maltose, sucrose, glucose and fructose were purchased from Sigma Aldrich , and isomaltulose from Myopure .4OH and Mobile Phase B: 20% RO water/80% acetonitrile with 0.1% NH4OH, utilising a gradient as follows: 0–1 min, 100% B; 1–13 min, 100% B to 50% B; 13—16 min, 50% B to 100% B; 16–20 min, 100% B.Honey samples (0.5 g) were dissolved in ultrapure water (50 mL) and further diluted 1:10 in water and then 1:4 in acetonitrile. Sugar standards were similarly dissolved in Millipore water and diluted sequentially in aqueous acetonitrile to provide a concentration range of glucose 1–204 µg/mL, fructose 1–195 µg/mL, sucrose 1–198 µg/mL and trehalulose 1–203 µg/mL. Analysis of sugars in individual honey samples was conducted on a Shimadzu Nexera ultra high-performance liquid chromatograph (UHPLC) coupled with a Shimadzu 8045 MS/MS detector with Lab Solutions software and a CTO-20AC column oven was operated at 35 °C. Separations were conducted on a Waters Acquity UPLC BEH Amide 1.7 µm, 2.1 × 100 mm column eluted at a flowrate of 0.2 mL/min with Mobile Phase A: 70% RO water/30% acetonitrile with 0.1% NH2) typically in the range of 0.98–0.99. Percentage recoveries of standard additions to five honeys at four levels for each of glucose, fructose, sucrose and trehalulose were calculated, with values for spiked samples calculated by subtraction of the endogenous, no-spike value. Recoveries for spiked samples were calculated by using the expected value and averaged 93–119% with standard deviations of 2–4% at the highest spike level, 4–15% at the intermediary spike levels and 18–40% at the lowest spike level.Method validation was conducted by comparing the results for calibration using external sugar standards to a standard additions method for determining sugar concentrations. For standard additions, squared linear correlation coefficients (R−) m/z 341.1 was fragmented at both 20 V and 8 V to examine differences in fragmentation of individual eluted disaccharides. Selected reaction monitoring (SRM) transitions of m/z 341.2 → 179.2 were used for quantitation of trehalulose in each of the analysed honeys with confirmation transitions of m/z 341.2 → 251.2 and m/z 341.2 → 161.2. Sucrose was quantitated using m/z 341.2 → 179.2, with m/z 341.2 → 161.2 and m/z 341.2 → 119.1 used for confirmation. Similarly, glucose and fructose were analysed based on SRMs of m/z 179.2 → 89.0 (quantitation) with m/z 179.2 → 101.1 and m/z 179.2 → 113.1 (confirmation).The disaccharide molecular ion  using full-scan ddMSMS mode using an inclusion list of 341.1089 ([M-H]−) and 387.1144 (M + HCOO−). The normalized collision energy (NCE) was set to 20%. Xcalibur  was used for instrumental control and spectral inspection.High resolution mass spectral data was conducted on a Thermo Dionex Ultimate 3000 ultra high-performance liquid chromatograph (UHPLC) coupled with a Q Exactive Orbitrap high resolution accurate-mass (HRAM) spectrometry system. LC separation was carried out on a Waters Acquity UPLC BEH Amide column  at 35 °C using a flowrate of 0.2 mL/min with mobile phase A: 70% RO water/30% acetonitrile with 0.1% NH2 100 Å 250 × 4.6 mm column with an isocratic mobile phase comprised of 85% acetonitrile and 15% RO water at a flow rate of 2.5 mL/min. Eluted sugars were monitored with a Shimadzu ELSD- LT (Low Temperature) detector operated at 350 kPa and 45 °C. The elution flow was diverted to a collection tube when the target peak emerged and 5 mL (2 min) fractions collected before reconnection of the column eluant to the ELSD detector (to see the tail end of the peak). Collected fractions were analysed ‘as is’ by UPLC-MS/MS (as above), and a second portion freeze-dried and dissolved in D2O for NMR analysis.To enable separation of individual disaccharides, the honey samples were chromatographed on a Shimadzu HPLC-ELSD system consisting of a Shimadzu Class VP Pump LC-10AD VP/Valve FCL-10AL VP/Degasser DGU-14A with a Class VP Software/ SCL-10A VP controller, SIL-10AD VP autosampler and a CTO-10A VP column oven operated at 40 °C. Separations were performed on a Phenomenex Luna 5 µm NH1H nuclear magnetic resonance (NMR) analysis performed on an AV500 (500.13 MHz) Bruker Avance system using 5 mm SEI Probe. A zg45 pulse experiment at 298 K with 64 scans and a spectral width of 12.0 ppm  was applied. Spectra were recorded in D2O, with chemical shifts (δ) values recorded in ppm. Residual protonated solvent signals were used as internal standard (δ 4.80). NMR data presented as: chemical shift, multiplicity , coupling constant (J Hz), assignment. Multiplicities, and hence coupling constants, reported are apparent values. 13C NMR spectra were recorded on a Bruker Avance 500 (125.77 MHz) spectrometer with complete proton decoupling. Spectra were recorded in D2O with added 1% 1,4-dioxane (δ 67.40) used as reference. 2D COSY, TOCSY, HSQC, HMBC (500 MHz) spectra were used to confirm spectral assignments. Trehalulose exists in aqueous solution as a mixture of both pyranose and furanose forms (3.6:1) with both tautomers assigned as below.The purified sugar was analysed via O-α-d-glucopyranosyl-β-d-fructopyranose (1a). 1H NMR  δ 4.98 , 4.08 , 4.02 , 3.94 , 3.92 , 3.88 , 3.85, 3.79 , 3.80–3.76 , 3.73 , 3.72 , 3.58 , 3.48 , 3.44 ppm . 13C NMR  δ 99.32 (C-1′), 98.64 (C-2), 73.81 (C-3′), 72.73 (C-5′), 72.28 (C-2′), 70.41 (C-4′), 70.34 (C-4), 69.93 (C-1), 69.89 (C-5), 68.70 (C-3), 64.35 (C-6), 61.31 ppm (C-6′).1-O-α-d-glucosylpyranosyl-β-d-fructofuranose (1b). 1H NMR  δ 5.01 , 4.13 (H-4), 4.15 (H-3), 3.89 (H-6′), 3.88 (H-5), 3.81 (H-6), 3.76 (H-3′), 3.72 (H-5′), 3.68 (H-6′), 3.59 (H-2′), 3.56 ppm (H-1). 13C NMR  δ 101.69 (C-2), 81.49 (C-5), 77.11 (C-3), 75.19 (C-4), 72.91 (C-5′), 72.23 (C-2′), 69.29 (C-1), 63.13 (C-6), 61.35 ppm (C-6′).1-1a) and (1b).NMR analysis of authentic trehalulose  provided matching NMR data for both tautomers (Supplementary Information.


Psilocybe, among others, and is the active ingredient in so-called “magic mushrooms”. Although its notoriety originates from its psychotropic properties and popular use as a recreational drug, clinical trials have recently recognized psilocybin as a promising candidate for the treatment of various psychological and neurological afflictions. In this work, we demonstrate the de novo biosynthetic production of psilocybin and related tryptamine derivatives in Saccharomyces cerevisiae by expression of a heterologous biosynthesis pathway sourced from Psilocybe cubensis. Additionally, we achieve improved product titers by supplementing the pathway with a novel cytochrome P450 reductase from P. cubensis. Further rational engineering resulted in a final production strain producing 627 ± 140 mg/L of psilocybin and 580 ± 276 mg/L of the dephosphorylated degradation product psilocin in triplicate controlled fed-batch fermentations in minimal synthetic media. Pathway intermediates baeocystin, nor norbaeocystin as well the dephosphorylated baeocystin degradation product norpsilocin were also detected in strains engineered for psilocybin production. We also demonstrate the biosynthetic production of natural tryptamine derivative aeruginascin as well as the production of a new-to-nature tryptamine derivative N-acetyl-4-hydroxytryptamine. These results lay the foundation for the biotechnological production of psilocybin in a controlled environment for pharmaceutical applications, and provide a starting point for the biosynthetic production of other tryptamine derivatives of therapeutic relevance.Psilocybin is a tryptamine-derived psychoactive alkaloid found mainly in the fungal genus  •De novo production of psilocybin in S. cerevisiae.•P. cubensis significantly boosts production.Expression of a novel cytochrome P450 reductase from •Rational metabolic engineering results in 627 mg/L psilocybin production.•Production of natural and new-to-nature tryptamine derivatives demonstrated including norbaeocystin, baeocystin, and aeruginascin. Psilocybin itself is not psychoactive - rather it is the dephosphorylated derivative psilocin that causes the hallucinogenic effect. Psilocybin is rapidly dephosphorylated to psilocin following ingestion in the mucosa by alkaline phosphatases and nonspecific esterases  Fig. 1)Fig. 1). 50 of 280–285 mg/kg in rats and mice  to make extraction a commercially viable option , with cuPsilocybin preparations for pharmaceutical and research use currently rely on chemical synthesis via a difficult and expensive process . AlthougP. cubensis was recently elucidated (Aspergillus nidulans was established with titers reaching 110 mg/L (Escherichia coli (E. coli) by the in vivo bioconversion of added substrates 4-hydroxyindole, serine and methionine, with a titer of 1.16 g/L reported  as previously reported ) to exponentially growing cells and aseptically storing 1 mL aliquots at −80ᵒC. Cultures were grown in synthetic medium according to the following recipes. Synthetic medium was prepared with 7.5 g/L (NH4)2SO4, 14.4 g/L KH2PO4, 0.5 g/L MgSO47H2O and appropriate growth factors (4)2SO4. YP medium was prepared with 10 g/L yeast extract and 20 g/L peptone. In all cases unless stated otherwise, 20 g/L glucose was added. Media was supplemented with 200 mg/L G418 and 100 mg/L nourseothricin when required. E. coli strains were grown in Luria–Bertani (LB) media and supplemented with 100 mg/L ampicillin when required. Agar plates were prepared as described above but with the addition of 20 g/L agar.All is study  were deris study . Frozen  factors . Synthet2.2E. coli DH5α was used for all plasmid cloning and propagation. Single integration plasmids were constructed using the EasyClone-MarkerFree system (S. cerevisiae genome was achieved using a Kluyveromyces lactis URA3 gene (KlURA3) under control of a truncated 10bp KlURA3 promoter. Heterologous genes were codon-optimized for expression in S. cerevisiae using the JCat algorithm  cultivation of psilocybin producing S. cerevisiae strains, cells were inoculated from a 400 μL synthetic media pre-culture into 500 μL synthetic media to a starting OD600 of 0.1 in a 96-deep well microtiter plate with air-penetrable lid  and incubated for 72 h with shaking at 300 rpm. When required, uracil was added to a final concentration of 200 mg/L.2, O2 and ethanol were monitored continuously , and data acquisition was achieved using the Lucullus software . Seed culture was prepared by cultivating the strain for 48 h at 30 ᵒc in 50 mL of synthetic media containing; 3 g/L KH2PO4, 0.5 g/L MgSO4.7H2O, 5 g/L (NH4)2SO4, 20 g/L glucose and appropriate vitamins and trace elements , fed-batch phase was initiated at a starting feed rate of 0.45 g/h with an exponential increase of 0.02 h−1), fed-batch phase was initiated at a starting feed rate of 0.45 g/h with an exponential increase of 0.02 h−1. Feed media contained; 30 g/L KH2PO4, 6.3 g/L MgSO4.7H2O, 50 g/L (NH4)2SO4, 0.35 g/L Na2SO4, 500 g/L glucose, 1 mL/L Antifoam 204 and a 10 fold higher vitamin and trace element concentration  equipped with measurement probes for pH, dissolved oxygen (DO) and temperature. During fermentation, off-gas COelements . The seentration . During Extraction of extracellular tryptamine derived metabolites for analysis was performed as follows. Cell culture broth was supplemented with 100% acetonitrile at a ratio of 1:1, vortexed thoroughly then centrifuged at 3000 g for 5 min. The resulting supernatant was further diluted in 50% acetonitrile (v/v) if required and analyzed using LC-MS with the following conditions; High resolution LC-MS measurements were carried out on a Dionex UltiMate 3000 UHPLC , connected to an Orbitrap Fusion Mass Spectrometer . The UHPLC was equipped with a SeQuant zic-Hilic column (Merck KGaA), 15 cm x 2.1 mm, 3 μm. The temperature was 35 °C and the flow rate 0.5 mL/min. The system was running an isocratic gradient with a mobile phase consisting of 20% 10 mM ammonium formate (pH 3) and 80% acetonitrile, with 0.1% formic acid. The samples were passed on to the MS equipped with a heated electrospray ionization source (HESI) in positive-ion mode with sheath gas set to 50 (a.u.), aux gas to 10 (a.u.) and sweep gas to 1 (a.u.). The cone and probe temperature were 325 °C and 350 °C, respectively, and spray voltage was 3500 V. Scan range was 100–800 Da and time between scans was 50 ms. Psilocybin, psilocin and tryptamine authentic analytical standards were obtained from Sigma-Aldrich and used to quantify production in engineered strains.Extraction of all other extracellular metabolites was performed by centrifuging cell cultures and collecting the resulting supernatant which were analyzed for quantification of ethanol (and other metabolites) as follows. Metabolites were detected and quantified using a high performance liquid chromatography (HPLC) Agilent 1100 series with a refractive index detector and a Bio-Rad Aminex HPX-87H column (300 mm × 7.8 mm) with 5 mM H2SO4 as an eluent at a flow rate of 0.6 mL/min with column oven temperature set to 50 °C. To protect the HPX-87H column from contamination and foreign particles, a guard column was fitted to the system.33.1S. cerevisiae strain ST8251 (CEN.PK113-5D + Cas9) (Catharanthus roseus (C. roseus) tryptophan decarboxylase (CrTdc) was used instead of the P. cubensis variant (PsiD), due to its previously confirmed efficacy in S. cerevisiae (PcPsiH). Cytochrome P450 enzymes are characterized by their dependency on a cytochrome P450 reductase (CPR), which facilitates electron transfer between NADPH and cytochrome P450 enzymes  or spontaneously degraded to psilocin, however the 4-hydroxytryptamine kinase (PcPsiK) has been shown to also act on psilocin, resulting in a psilocybin-psilocin equilibrium reaction  . The biorevisiae . Tryptam enzymes . In S. creaction . Finallyreaction .S. cerevisiae (resulting in strain ST9327), then, using an Orbitrap Fusion Mass Spectrometer and authentic analytical standards, successful production of psilocybin, as well as the pathway intermediate tryptamine, and the spontaneous degradation product psilocin was confirmed in micro-titer plate cultivation (The basic heterologous pathway was introduced into tivation . Quantif3.2PcpsiH. Cytochrome P450 enzymes (CYP) belong to a superfamily of heme-containing monooxygenases and require a cytochrome P450 reductase (CPR) partner to deliver one or more electrons to reduce the heme-bound iron and oxidized substrates (S. cerevisiae CPR (Ncp1) could carry out this reduction, the accumulation of tryptamine suggested a sub-optimal interaction between the two enzymes. Due to the importance of the CYP-CPR interaction for optimal catalytic activity  with a tBLASTx search of the P. cubensis genome  AtAtr2 was also tested with additional overexpression of the S. cerevisiae cytochrome b5 (CYB5) as this has previously been shown to enable functional expression of other CYP's , and expression of Pccpr from the medium-strength TEF2 promoter only a small increase in titer (ST9329), expression of Pccpr from the strong TEF1 promoter produced a significant increase in psilocybin and psilocin titer (ST9328), reaching 137.1 ± 8.3 mg/L and 82.8 ± 3.7 mg/L respectively in micro-titer plate, representing a 29-fold increase over the parental strain  led to only a minor increase in titer.While psilocybin was successfully produced in yeast, the initial titers were low. Furthermore, analysis revealed the extracellular accumulation of tryptamine (120.3 ± 11.1 mg/L) indicating a significant limitation in the conversion of tryptamine to 4-hydroxytryptamine encoded by the cytochrome P450 gene bstrates . While tactivity , we attes genome  using Ncer CYP's . Finallyl strain A. This n3.3PcCpr resulted in a significant increase in psilocybin and psilocin titers, and furthermore, significantly reduced extracellular accumulation of the first product in the heterologous pathway, tryptamine , overexpression of feedback insensitive mutant genes encoding proteins in the shikimate pathway  (RIC1) (RIC1 (resulting in strain ST9179) and subsequent overexpression of ARO1 and ARO2 (resulting in strain ST9316) lead to a significant increase in psilocybin and psilocin titers. While further overexpression of feedback insensitive Aro4K229L, Trp2S65R, S76L (resulting in strain ST9318) resulted in a decrease in titer. In all three strains a significant increase in the LC-MS peak areas corresponding to norbaeocystin and baeocystin was observed compared to the parental strain containing the psilocybin biosynthetic pathway and optimized Pccpr expression (ST9328)  , and del) (RIC1) . Iterati(ST9328) . While ibased on  using a ectively B.PcpsiK on a Ty-integrative vector . By cultivating the strain in bioreactors, the pH, oxygen supply and aeration could be accurately controlled and higher glucose amounts could be fed, which should allow for comparatively higher production compared to micro-titer plate cultivation. Fed-batch fermentation was performed in triplicate with an initial exponential feed-rate of 0.02 h−1 with a 513 g/L glucose feed media . In total, 266.6 ± 5.2 g/L of glucose was fed over a period of 213.7 h. Controlled fed-batch fermentation of ST9482 led to the production of 627 ± 140 mg/L of psilocybin and 580 ± 276 mg/L of psilocin  accepting both tryptophan and 4-hydroxytryptophan (PcPsiK) (ST9441). Interestingly, omitting this gene abolished all production indicating that PcPsiM only accepts phosphorylated 4-hydroxytryptamine substrates (Pccpr expression) tentatively  detected the presence of the pathway intermediates norbaeocystin (non N-methylated)  led to a 21-fold increase in the dephosphorylated aeruginascin peak area , previously demonstrated to convert 5-hydroxytryptamine (serotonin) into N-acetyl serotonin (normelatonin) in S. cerevisiae (N-acetyl-4-hydroxytryptamine in the constructed strain (ST9442) demonstrating not only the relaxed substrate specificity of BtAANAT, but also the successful production of a novel molecule structurally similar to both psilocin and normelatonin with potentially novel pharmacological activity  A, baeocyhylated) B and psihylated) C catalyzginascin . Intereseak area , therebyrevisiae  could alactivity E.Fig. 6P4Saccharomyces cerevisiae, demonstrating a feasible option for the future production of this pharmaceutically interesting molecule. While introducing the basic biosynthetic pathway into yeast led to the production of psilocybin, high-level production, and corresponding consumption of excess tryptamine was achieved only upon expression of a putative cytochrome P450 reductase (Pccpr) from the host species P. cubensis. Functional expression was also dependent on the level of Pccpr transcription, with expression from the strong constitutive promoter pTEF1 resulting in an increase in psilocybin titers (ST9328) while expression from the mid-strength constitutive promoter pTEF2 resulted in only a small increase in psilocybin titers (ST9329) (S. cerevisiae CPR (NCP1) expressed both from its native promoter and strong constitutive promoter pTEF1 as well as A. thaliana CPR (AtAtr2) are both poorly compatible with the P. cubensis CYP (PcPsiH) is completely in-line with previous studies and demonstrates the importance of investigating the CYP:CPR interaction carefully (PcpsiH required strong constitutive expression of Pccpr from pTEF1. Excess expression of CPR's are often toxic to the cell as it leads to electron transfer uncoupling and the production of reactive oxygen species (ROS). For this reason, most native and heterologous CYP enzymes are expressed far in excess of their CPR partner  A. Overalarefully . Interes partner . The obs partner . This fuRIC1 produced an 18% increase in titer (ST9179) , a further substantial increase was achieved by introducing multiple copies of PcpsiM (ST9492)  may be explained by the increased accumulation of pathway intermediates baeocystin and norbaeocystin and their possible negative impact on cell physiology. Indeed a significant decrease in final OD600 was observed for this strain which subsequently increased after introducing multiple copies of PcpsiM (data not shown) and observing a decrease in the LC-MS peak areas of baeocystin and norbaeocystin  B. This gnic acid  suggesti(ST9492) B indicateocystin . While igrations . HoweverS. cerevisiae contains multiple phosphatase enzymes capable of removing phosphate groups from proteins and small-molecules  and aeruginascin  provided a proof-of-concept for industrial-scale production, and while separation and purification of psilocybin from the fermentation broth were not investigated in this work, the observation that psilocybin was efficiently excreted into the extracellular space by yeast will facilitate a simple and cost-effective downstream processing procedure. While we were able to demonstrate high-level psilocybin production (627 ± 140 mg/L in fed-batch fermentation), this was accompanied by concomitant production of psilocin (580 ± 276 mg/L). In fact, significant accumulation of psilocin was observed in all strains tested  indicatiolecules , which molecules . Indeed . Indeed  showed tter 139h  suggestiginascin , and then titers , suggestnidation , it can S. cerevisiae to produce derivatives from 4-hydroxylated substrates. It was found that omitting PcPsiK resulted in no production of psilocybin or psilocin phosphorylate alternate substrates. Production of Psilocybe derived tryptamine molecules in S. cerevisiae further offers the possibility of isolating useful quantities of these molecules in high purity thereby facilitating increased investigation into their in vivo function, which, so far has been severely limited by their availability.While psilocybin is an interesting and pharmaceutically relevant molecule in its own right, one of the unique features of synthetic biology is the ability to “mix and match” enzymes to create new interesting molecules that aren't found in nature and are infeasible to produce by chemical synthesis. Tryptamine derivatives are an interesting class of molecule that are known to exert their effect through interaction with different serotonin receptors in the body . While mpsilocin  indicatiin vitro . Our effinascens . Interesngestion . This dingestion , and theN-acetyl-4-hydroxytryptamine was produced by introducing a serotonin N-acetyltransferase into a 4-hydroxytryptamine producing strain (ST9442) thereby resulting in a (to our knowledge) novel molecule structurally similar to the human neurotransmitter N-acetylserotonin (normelatonin) with the hydroxyl group on the 4 position instead of the 5 position. While it would be premature to speculate on possible pharmacological functions of this new molecule, it demonstrates how in vivo enzymatic biosynthesis can be used to create novel structural variants of molecules that would otherwise be too complex to produce by chemical synthesis. Indeed, stereoselective introduction of hydroxyl groups into aromatic rings has been described as “one of the most challenging fields in modern  synthesis” (S. cerevisiae strains in the drug discovery process.nthesis” , thereby5Saccharomyces cerevisiae. Implementation of the biosynthetic pathway from Psilocybe cubensis with expression of a novel cytochrome P450 reductase resulted in high-level de novo production from glucose. Further rational engineering of native S. cerevisiae metabolism demonstrated how improved production can be achieved by boosting the native supply of the pathway precursor tryptophan. Finally, the production of natural and new-to-nature 4-hydroxytryptamine derivatives demonstrates the versatility of enzymatic biosynthesis, and how S. cerevisiae can be used to produce novel drug candidates that go beyond what is feasible with chemical synthesis or extraction from natural sources.In this work, the successful production of psilocybin was demonstrated in N. Milne: Conceptualization, Methodology, Validation, Investigation, Writing - original draft, Writing - review & editing. P. Thomsen: Validation, Investigation, Visualization. N. Mølgaard Knudsen: Investigation, Visualization, Validation. P. Rubaszka: Validation, Investigation. M. Kristensen: Methodology, Validation, Investigation. I. Borodina: Funding acquisition, Project administration, Supervision, Writing - original draft.NM and IB are co-inventors on a patent application related to this research.


In recent years, many researchers have shown increasing interest in music information retrieval (MIR) applications, with automatic chord recognition being one of the popular tasks. Many studies have achieved/demonstrated considerable improvement using deep learning based models in automatic chord recognition problems. However, most of the existing models have focused on simple chord recognition, which classifies the root note with the major, minor, and seventh chords. Furthermore, in learning-based recognition, it is critical to collect high-quality and large amounts of training data to achieve the desired performance. In this paper, we present a multi-task learning (MTL) model for a guitar chord recognition task, where the model is trained using a relatively large-vocabulary guitar chord dataset. To solve data scarcity issues, a physical data augmentation method that directly records the chord dataset from a robotic performer is employed. Deep learning based MTL is proposed to improve the performance of automatic chord recognition with the proposed physical data augmentation dataset. The proposed MTL model is compared with four baseline models and its corresponding single-task learning model using two types of datasets, including a human dataset and a human combined with the augmented dataset. The proposed methods outperform the baseline models, and the results show that most scores of the proposed multi-task learning model are better than those of the corresponding single-task learning model. The experimental results demonstrate that physical data augmentation is an effective method for increasing the dataset size for guitar chord recognition tasks. Automatic chord recognition (ACR) is a fundamental problem in the music information retrieval (MIR) field, wherein a chord is one of the key elements for understanding music. ACR remains a challenging problem because of the richness of the acoustic signals, broad types, and complexity of the music signal. The purpose of chord recognition is the automatic recognition of the chord progression in music recordings and its labeling in an appropriate form, such as A:maj, A:min, and B:sus. Popular applications of ACR can be found in music identification, music segmentation, and similar music recommendation systems. Recent ACR studies have shown that the chord recognition performance can be significantly improved using deep learning models, especially convolutional neural networks (CNNs) and recurrent neural networks (RNNs) ,2. Many An ACR system consists of three main components, namely feature extraction, classifiers, and chord sequence decoding. In the feature extraction part, an input audio signal is converted to its time-frequency representation using a short-time Fourier transform or frequency spectrogram. The log mel-spectrogram and constant Q transform (CQT) are widely used features in chord recognition ,5. MoreoDeep neural networks (DNNs) and CNNs are widely used for classification in audio music classification tasks ,8. In reIn this study, we used two types of datasets, namely the GuitarSet  and physhttps://github.com/gerelmaab/Physically_augmented_guitar_chord_dataset) a large-vocabulary dataset of guitar chords, which is created by the physical data augmentation method.5.In this paper, we address the problem of the relatively large-vocabulary chord recognition task by introducing multi-task deep learning architecture that learns the chord roots and qualities individually, and the output is integrated onto the symbolic label. Furthermore, we sought to generate an augmented dataset using robotics and prove its effectiveness via experimental evaluations. We also aimed to construct and open . The output root note and quality, respectively, are estimated/generated and then combined into one label. The output of the learning models is converted into label files to evaluate the performance of the architectures. We tested the recognition system only on a human dataset.The robot system comprises a string-pushing unit, picking unit, and linearly moving mechanical unit. A snapshot of the developed guitar-playing robot system is shown in The guitar-playing robot pushes the strings correctly at the right positions and subsequently generates various types of tones from the guitar. For the string-pushing unit, a four-line solenoid bar with six solenoids used in each line to fit six strings were installed; in total, there are 24 solenoids, so that any variation of a chord can be played as shown in Hence, the input force  feature for speech recognition tasks along with their delta and delta-delta to describe the acoustic energy distribution in each of the several different frequency bands . This reLibrosa  computedThe neighboring frames of the input representation can be expected to contain similar content, as the chords will not change on a frame-by-frame basis. Through systematic experiments on the validation folds , we founIn the dataset annotation, chord description is represented as G#:maj6/1. To formulate chord recognition as a classification task, we define a mapping of the dataset from chord description to chord vocabulary. First, we discard the chord inversions and suppressed or additional notes. For example:D#:sus2(7)/1 → D#:sus2maj6 → majmin6 → minmaj9, minmaj7 → maj7min9, min11 → min79, 11 → 7dim → dim7Next, we introduce some conversion from the chord label with a short duration to the analogy label. We use ten types of quality, including maj, min, aug, maj7, min7, 7, dim7, hdim7, sus2, and sus4. The final vocabulary contains 98 classes. We incorporate a conversion over a chord as follows:For the labeling, a single label is estimated with the superframe. The chord label of the middle frame is used to the audio context clip label as illustrated in The GuitarSet dataset: The GuitarSet contains musical audio files and the corresponding chord annotations, which were annotated with [ted with . Three cThe augmented dataset: The augmented dataset was recorded directly from the guitar playing robot. This dataset consists of 12 root notes and 10 types of chord quality in a total of 97 classes of chords. Each chord was played individually with five types of stroking patterns, including D DU DU DU D, D DU UDU, D D DU UDD, D D DU, and simple fingerstyle, where D represents a downstroke, U represents an upstroke, and a space represents the gap time between the strokes. The recording environment was a sound isolation chamber. The microphone was placed near the guitar sound hole. Each specific chord was recorded for approximately 40–45 s, indicating that the created augmented dataset was evenly distributed over all the chord types. This dataset was recorded in an identical manner as the human dataset, in the WAV format and sampled at fs = 44,100 Hz. We annotated the chords manually using the chord annotation method proposed by [posed by .The Guitarset dataset is an unbalanced dataset over the chords, e.g., the C#:aug and C#:sus4 chords have the least duration of approximately 1.2 s; in contrast, the C#:maj chord has the longest duration of approximately 380 s. The distribution of the chord types of the Guitarset dataset is shown in This section details single-task learning and the structure of the proposed MTL model.The single-task learning network contains seven convolutional layers, two recurrent layers, and two fully connected (FC) layers. The architecture of the proposed convolutional recurrent neural network (CRNN) is shown in Training deep learning systems usually requires large and balanced datasets to train a good system and learn accurate parameters. However, in some applications such as medical imaging or where there is a relative lack of data  this requirement cannot be satisfied. In a large-vocabulary chord dataset, the majority class examples, such as major and minor, far outnumber the minority class examples such as suspended or augmented. In these cases, the single-task learning model lacks knowledge of some classes, and accurate learning is difficult. However, multi-task learning is a useful approach in cases where useful information can be derived from other related learning tasks to handle the data sparsity problem.The proposed model learns a mapping between an audio representation, such as the CQT, and the root notes and qualities. Essentially, the model can be viewed as a multi-task leaning model. From the pre-processed input, the proposed model computes the root and quality outputs directly. The model is trained with a multi-task learning method using two cost functions. In each cost function, the cross entropy (CE) between the ground truth and predicted labels is used. In summary, the root note and quality are modeled jointly with a shared network by the proposed model. This learning method is designed for chord recognition.In the case where the labeled data for one of the tasks are scarce, MTL can be adapted to the labeled data of the related task. In our dataset, the suspended, augmented quality training examples are short compared with the other classes of quality. As the two tasks have a different number of outputs, the potentially shareable layers are the middle layers of the model, resulting in a final layer of different dimensionality. In the proposed model, one sub-network learns 12 root notes, while the other sub-network learns 10 types of qualities, including major, minor, dominant seventh, dominant major and minor seventh, suspended, augmented, and half diminished 7 and diminished 7. The outputs of the two networks are then combined to obtain the ”root:quality” annotation form, which was proposed in . In thisWe combined MTL with the CNN and RNN framework by sharing some layers between the two related tasks. The input features are passed through two identical sub-networks, where each sub-network architecture is the same as a single-task learning model except for the output layer. The two identical architectures perform root note and quality recognition. The output layers have a different number of outputs. The output of the first task is 12 because there are twelve music notes. The output of the second task is 10 because ten types of chord qualities are employed. The proposed MTL model is shown in The MTL model has one shared layer and uses soft parameter sharing in the FC layers of the two sub-networks. The output of the FC layer of the root learning sub-network is concatenated to the output of the FC layer of the quality learning sub-network. Therefore, the classification of quality has some information of the chord root.In the experiment, 6-fold cross-validation was applied to the entire dataset. In each training fold, five players’ recordings were used for training, while the other was used for testing the dataset. First, the complete network was trained only with the human dataset (Guitarset dataset). Subsequently, the physical data augmented dataset was combined with the training subset of the human dataset, and the same experiment as the first was performed. In the second experiment, the test subset only consisted of the human played chords, similar to the first experiment.In the training phase, a frame-based strategy was used, wherein each song was divided into frames, and each frame was treated as an independent training sample. On average, each song was divided into approximately 250–350 frames, resulting in approximately 32,500 and 75,000 training samples in the human and augmented datasets, respectively, and approximately 9800 test samples.The neighboring frames of the input representation can be expected to contain similar content, as the chords will not change on a frame-by-frame basis. Therefore, the models were trained with 11 frames (of 1 s duration). The networks were implemented using the Keras library that runs on top of TensorFlow. The Adam optimizer  was emplct is the duration of the correctly classified chord segments and at is the duration of the entire chord segments.Weighted chord symbol recall (WCSR) was used for evaluation. The WCSR score can be computed using Equation , where tThe WCSR score was computed with mir_eval . The rooIn this experiment, cross-validation was not employed. In both the model cases, the GuitarSet dataset was used to train and test the models. Five players’ recordings were used as the training dataset, and the other player’s recordings were used as the test dataset. The CNN model is a VGG-style  CNN. TabHere, a VGG-style CNN architecture was used to evaluate the context size. We determined the optimal amount of context for each score experimentally using the one-fold experiment, as shown in In this experiment, we used the simple CNN model with the CQT and delta and delta-delta features, along with a small dataset. In the data augmentation method, time-stretching data augmentation was used, which slows down or speeds up the audio sample while keeping the pitch unchanged. Each sample was time-stretched using four randomly selected factors: 0.8, 0.9, 1.1, and 1.2. Four types of metrics were used for comparison because a small dataset was employed. Here, we compare the performance of the proposed and baseline methods on the same datasets. It is not simple to define a particular method as a state-of-the-art method because the chord recognition methods were evaluated on different datasets. Among the different models, we chose four baseline methods, namely the deep neural network (DNN) model, VGG-style CNN model, CNN  model, aThe experimental results of the baseline models and proposed methods are presented in When comparing the proposed methods, the MTL model trained with the augmented dataset, which was proposed method 3, obtained better performance in terms of four metrics, namely the root, thirds, tetrads, and maj-min. The other metrics showed comparable performance with the proposed methods. The STL performed better for the triad, sevenths, and MIREX. The proposed method achieved better performance than the baseline models and single-task learning model. The MTL models achieved better scores for chord root and triad recognition.Based on the above results, a CNN proved more effective for the chord recognition task compared with the traditional DNN learning method. The proposed MTL model based on the CNN and RNN networks showed good performance for the large-vocabulary guitar chord recognition task. The presented results clearly demonstrate that the performance of the proposed method was superior to that of the baseline models and single-task learning model. We demonstrated that the static feature along with the dynamic (delta) and acceleration (delta-delta) features are useful for the guitar chord recognition task. In contrast, the physical data augmentation method showed acceptable performance in the guitar chord recognition task. This physical data augmentation method can be used for any musical instrument. Using a robotic performer, we can create a large-sized and balanced dataset, which also includes sufficient training examples of the rarely played chords such as the sustained and eleventh. However, there are some limitations, including the hardware implementation and complexity of the robot design.In this paper, we applied a multi-task learning model based on a CNN and RNN to a relatively large-vocabulary guitar chord recognition task and demonstrated the physical data augmentation method and its utility/performance. The experimental results demonstrated that the multi-task learning model outperformed the baseline models and achieved better performance than its equivalent single-task model. In addition, we found that an augmented dataset created by a robot is also efficient for guitar chord recognition. Both dynamic and acceleration features were used as the model’s input features, resulting in good performance. The models trained by a human with the augmented dataset achieved improved performance in terms of most of the metrics. An advantage of multi-task learning for chord recognition is that its training speed is faster than that of single-task learning.